首页|Distilling seed-assisted zeolite synthesis conditions by machine learning
Distilling seed-assisted zeolite synthesis conditions by machine learning
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NSTL
Traditional zeolite synthesis requires usage of the organic structure-directing agent(OSDA)which is capital-intensive and non-eco-friendly.Seed-assisted zeolite synthesis can reduce or eliminate OSDAs,making it a green and economical approach.Seed-assisted synthesis conditions reside in a high-dimensional chemical space where a brute-force search is unfeasible.Here we utilize archived experiment records to build machine learning models for predicting crystallization tendency.Effects of seed physicochemical properties,gel compositions as well as crystallization conditions are calibrated.In particular,seed framework density,NaOH concentration in the gel and crystallization time have synergistic influences on the zeolite transformation.The machine learning model is interpreted with domain knowledge to obtain chemical hypothesis.The hypothesis is verified by follow-up experiments,distilling zeolite transitions that had been overlooked previously.
ZeolitesSeed-assisted synthesisMachine learning
Duozheng Ma、Xin Li、Jun Liang
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School of Chemical Engineering,East China University of Science and Technology,Shanghai,200237,PR China
State Key Laboratory of Green Chemical Engineering and Industrial Catalysis,Sinopec Shanghai Research Institute of Petrochemical Technology,Shanghai,201208,PR China
NSTL
